Can Codestral Mamba 7B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

A71Great

Estimated from fit model

Codestral Mamba 7B needs ~16.0 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.0 GB, 62.5 tok/s, Runs well

16.0 GB required69.1 GB available

23% VRAM used

Fit status

Runs well

Decode

62.5 tok/s

TTFT

3098 ms

Safe context

262K

Memory

16.0 GB / 69.1 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on MacBook Pro M2 Max 96GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 62.5 tok/s decode · 3.1s TTFT (warm) · 156 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	62.5 tok/s	1690 ms	262K
Coding	A	Runs well	62.5 tok/s	3098 ms	262K
Agentic Coding	A	Runs well	62.5 tok/s	4507 ms	262K
Reasoning	A	Runs well	62.5 tok/s	3662 ms	262K
RAG	A	Runs well	62.5 tok/s	5634 ms	262K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4	3.9 GB	Medium	B65
Q4_K_M	4	4.3 GB	Medium	B65
Q5_K_M	5	5.0 GB	High	B65
Q6_K	6	5.7 GB	High	B65
Q8_0	8	7.5 GB	Very High	B66
F16Best for your GPU	16	14.3 GB	Maximum	B67

Get started

Copy-paste commands to run Codestral Mamba 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
  --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M2 Max 96GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	35.1 tok/s
Qwen 3.5 27B	27B	S	15.2 tok/s
Qwen 3.6 27B	27B	S	11.6 tok/s
Qwen 3.6 35B A3B	35B	S	32.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	36.3 tok/s

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Codestral Mamba 7B?

Yes, MacBook Pro M2 Max 96GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 62.5 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral Mamba 7B?

The recommended quantization for Codestral Mamba 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral Mamba 7B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Codestral Mamba 7B achieves approximately 62.5 tokens per second decode speed with a time-to-first-token of 3098ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on MacBook Pro M2 Max 96GB receives a A grade with 62.5 tok/s and 262K context.

What context window can Codestral Mamba 7B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Codestral Mamba 7B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Codestral Mamba 7B?

Not always. MacBook Pro M2 Max 96GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M2 Max 96GB See all hardware for Codestral Mamba 7B

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